Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery

TL;DR

This paper introduces a deep learning framework using convolutional neural networks to generate detailed, global maps of human settlements from Sentinel-2 satellite imagery at 10-meter resolution, aiding urbanization policies.

Contribution

It presents a lightweight CNN-based method for pixel-wise classification of built-up areas, achieving global coverage and validated against independent building footprint data.

Findings

Produced the most detailed global built-up map for 2018

Validated model accuracy with 277 worldwide sites

Demonstrated robustness and reliability of the approach

Abstract

Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world.The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale.This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery.A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed.The core features of the proposed model are the image patch of size 5 x 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers.The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference data-set of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness.